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Decreasing Falls with the Automation of
Beers Criteria Medications
Session #312, February 15, 2019
Siraj Anwar, Associate Chief Medical Informatics Officer
Memorial Hermann Health System
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Siraj Anwar, MBBS, MS, CPHIMS
Has no real or apparent conflicts of interest to report.
Conflict of Interest
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• Background: Elderly and Medication Use
• Introduction to Beers Criteria/ List
• Governance Model
• HIT Interventions
• Measuring Success
• Closing Thoughts
Agenda
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• Discuss how Clinical Decision Support can
improve safety, quality and cost-effectiveness of
patient care
• Explain how the EHR’s ability to add filters for
age and conditions, as well as dose range
checking alerts, improves the automation and
reliability of the CDS alerts
• Describe the importance of governance when
using CDS
Learning Objectives
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292 Care Delivery Sites
Our Network of Care
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Background
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What percent of the U.S. population is over
65 years of age?
~15.6%*
~51 Million
Background
* United States Census Bureau
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Background
Hospitalizations, by age: United States, 2000-2010
SOURCE: CDC/NCHS, National Hospital Discharge Survey, 2000–2010
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Adverse Drug Events
• ~770,000 people are injured or die annually
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• Spend 8-12 days longer in hospital
• Costs $16,000-$24,000 +
• 9.7% of ADEs result in permanent disability
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References
1. Classen DC, Pestotnik SL, Evans RS, et al. Adverse drug events in hospitalized patients. JAMA
1997;277(4):301-6.
2. Cullen DJ, Sweitzer BJ, Bates DW, et al. Preventable adverse drug events in hospitalized patients: A
comparative study of intensive care and general care units. Crit Care Med 1997;25(8):1289-97.
3. Cullen DJ, Bates DW, Small SD, et al. The incident reporting system does not detect adverse drug events: A
problem for quality improvement. Journal on Quality Improvement 1995;21(10):541-8.
4. Thomas EJ, Studdert DM, Burstin HR, et al. Incidence and types of adverse events and negligent care in Utah
and Colorado. Med Care 2000;38(3):261-71
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Adverse Drug Events
• Adverse Drug Events
– Common in elderly
– Important cause of morbidity and death
• Type “A”
– Dose related
• Predictable
• Potentially avoidable
Beers Criteria/List
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Beers Criteria/List
What it’s not….
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Beers Criteria/List
• Originally conceived in 1991 by Mark Beers,
MD (geriatrician)
• 1991 1997 2003 2012 2015
• Identifies medications that
pose potential risks
outweighing potential
benefits for people ≥65 years
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Beers List – Intended Use
Goal: To improve care of older adults by
decreasing exposure to potentially
inappropriate medications (PIM)
• Risks outweigh the benefits
• Not meant to be punitive
• Does not supersede clinical judgment
• Individual patient’s values & needs
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Evidence Base
LESS IS MORE
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Shabot “Let’s not Boil the
Ocean” Beers List Proposal
• Design CDS interventions to help
prevent falls, fractures and deaths in
elderly
• Create a review panel
– Clinicians
– Pharmacists
– Informaticists
• Identification of target medications
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Governance
• Clinical Decision Support Oversight
• Beers Criteria Workgroup
– Physicians
– Pharmacists
– Medical Informatics
• Acute Care Medical Informatics Committee
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Key Decisions
• Target Medications
– Phased Approach
– Alternative Medications
• CDS Interventions
– Alerts
– Order Sets
– Order Sentences
• Measuring Processes
• Measuring Outcomes
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IT Interventions
• Alerts
– Notification alerts for using caution
– Alternative medications included
– Pharmacist consults
• Update Order Set Content
– Add comments on PIM for elderly
– Create “smart” orders
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Beers Criteria Alert
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Pharmacist Consult
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Order Set
Smart Orders
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Order Set
Smart Orders
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Alert Data
Daily/ Monthly Trends
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Alert Data
Override Rate/ Alert Volume
Facility 1
Facility 2
Facility 3
Facility 4
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Outcome Measures
• Good Catches
– Alerts backed out and therapy changed
• Volume of orders for elderly patients
– Are we seeing more elderly patients (>65 yrs)?
– # of Orders per 1000 elderly patients (>65 yrs)
• Serious Safety Events
– Elderly patient falls
– Related to Beers list medications
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Good Catches
Facility 1
Facility 2
Facility 3
Facility 4
Total of 39,172 Good Catches since
alert implementation
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Orders Data: 7 Drugs
Account for 80% of all orders
~28% Reduction
Alert Go-Live (4/15/14)
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Prevention
Assumption
Estimated
ADE’s
Estimated Cost
Savings
Estimated
Lives Saved
Estimated
SSEs
Prevented
ADE’s prevented per
potential medication
error prevented
Good
Catches
N=39,172
$ 2,595 per
adverse drug
event
1% 12%
1 in 2 19,586 $ 50,825,670 196 2,350
1 in 4 9,793 $ 25,412,835 98 1,176
1 in 8 4,897 $ 12,707,715 49 588
1 in 16 2,448 $ 6,352,560 24 288
1 in 32 1224 $ 3,176,280 12 144
Financial Impact
PIM in Elderly Patients
Estimates based on Bates DW, et al. JAMA 1997;277(4): 307-311 and JAMA 1995:274(1): 29-34
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Closing Thoughts
• “To”
• Governance
• Measure, measure and measure
• Talk about the success
vs. “With”
